Exploratory Data Analysis for HDB Resale Prices Dataset

Note: All exploratory data analysis done in this notebook will be based on the time period: 2003 to 2012

In this jupyter notebook, we will attempt to use data visualization tools to explore the relationships between features (both pre-defined by the original dataset and self-defined by us) and the corresponding HDB resale prices in Singapore. This data exploration process is done after Data Cleaning & Feature Engineering.

This is a complete and more detailed exploratory data analysis on the HDB Resale Price Dataset, of which only some features have been selected to be presented in the Final Report due to space constraints and importance of other aspects of the project.

After which, we will compare the trends across the two time periods in the Final Report.

1. Importing Necessary Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
import matplotlib as mpl

import seaborn as sns
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

import random

%matplotlib inline

2. Loading HDB Resale Prices Dataset

hdb_price_df_03_12: Pandas DataFrame object representing the features and information related to HDB flats in Singapore, for the time period — 2003 to 2012 (10 years)

hdb_price_df_13_23: Pandas DataFrame object representing the features and information related to HDB flats in Singapore, for the time period — 2013 to February 2023 (10 years & 2 months)

hdb_price_df: Pandas DataFrame object will be the combination of both DataFrames — hdb_price_df_03_12 & hdb_price_df_13_23

hdb_price_df_03_12 = pd.read_csv('hdb-price-with-features-2003to2012.csv').iloc[:, 1:]
hdb_price_df_13_23 = pd.read_csv('hdb-price-with-features-2013to2023.csv').iloc[:, 1:]

hdb_price_df_03_12.head()

	month	town	flat_type	block	street_name	storey_range	floor_area_sqm	flat_model	lease_commence_date	remaining_lease	...	mall_count_within_1km	mall_list_within_1km	school_shortest_dist	school_shortest_dist_name	good_sch_count_within_1km	good_sch_list_within_1km	good_sch_score	good_sch_score_sap_gep	distance_to_cbd_km	matured
0	2012-09-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	85.0	...	1	['Junction 8']	0.214393	Catholic High School	1	['Catholic High School']	3	2	7.635584	1
1	2012-03-01	BISHAN	4 ROOM	289	BISHAN ST 24	01 TO 05	102.0	Premium Apartment	1998	85.0	...	1	['Junction 8']	0.214393	Catholic High School	1	['Catholic High School']	3	2	7.635584	1
2	2012-03-01	BISHAN	5 ROOM	289	BISHAN ST 24	11 TO 15	121.0	Premium Apartment	1998	85.0	...	1	['Junction 8']	0.214393	Catholic High School	1	['Catholic High School']	3	2	7.635584	1
3	2012-02-01	BISHAN	5 ROOM	289	BISHAN ST 24	07 TO 09	121.0	Premium Apartment	1998	85.0	...	1	['Junction 8']	0.214393	Catholic High School	1	['Catholic High School']	3	2	7.635584	1
4	2011-12-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	86.0	...	1	['Junction 8']	0.214393	Catholic High School	1	['Catholic High School']	3	2	7.635584	1

5 rows × 30 columns

# new 'year' feature - just to check correctness of time frame 
hdb_price_df_03_12['year'] = pd.DatetimeIndex(hdb_price_df_03_12['month']).year
print("Range of Years in \x1B[3mhdb_price_df_03_12\x1B[0m DataFrame:", np.sort(hdb_price_df_03_12['year'].unique()))

hdb_price_df_13_23['year'] = pd.DatetimeIndex(hdb_price_df_13_23['month']).year
print("Range of Years in \x1B[3mhdb_price_df_13_23\x1B[0m DataFrame:", np.sort(hdb_price_df_13_23['year'].unique()))

Range of Years in hdb_price_df_03_12 DataFrame: [2003 2004 2005 2006 2007 2008 2009 2010 2011 2012]
Range of Years in hdb_price_df_13_23 DataFrame: [2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023]

3. Adjustment of HDB Resale Prices for Inflation (using CPI)

adjusted_price: indicates the adjusted HDB Resale Price after accounting for inflation

# Read CPI data
cpi = pd.read_csv('monthly cpi.csv')
cpi = cpi.set_index('Data Series')
cpi_data = pd.DataFrame(cpi.columns, columns=['month'])
cpi_data['CPI'] = pd.to_numeric(np.array(cpi.loc['All Items']))
cpi_data = cpi_data.iloc[1: , :]
cpi_data['month'] = pd.to_datetime(cpi_data['month'])

# adjust prices for inflation using CPI
hdb_price_df_03_12['month'] = pd.to_datetime(hdb_price_df_03_12['month'])
hdb_price_df_03_12 = pd.merge(hdb_price_df_03_12, cpi_data, how = 'left')
hdb_price_df_03_12['adjusted_price'] = (hdb_price_df_03_12['resale_price'] / hdb_price_df_03_12['CPI']) * 100

hdb_price_df_13_23['month'] = pd.to_datetime(hdb_price_df_13_23['month'])
hdb_price_df_13_23 = pd.merge(hdb_price_df_13_23, cpi_data, how = 'left')
hdb_price_df_13_23['adjusted_price'] = (hdb_price_df_13_23['resale_price'] / hdb_price_df_13_23['CPI']) * 100

hdb_price_df_03_12.head()

	month	town	flat_type	block	street_name	storey_range	floor_area_sqm	flat_model	lease_commence_date	remaining_lease	...	school_shortest_dist_name	good_sch_count_within_1km	good_sch_list_within_1km	good_sch_score	good_sch_score_sap_gep	distance_to_cbd_km	matured	year	CPI	adjusted_price
0	2012-09-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	97.298	657773.027195
1	2012-03-01	BISHAN	4 ROOM	289	BISHAN ST 24	01 TO 05	102.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	630205.762181
2	2012-03-01	BISHAN	5 ROOM	289	BISHAN ST 24	11 TO 15	121.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	766750.343987
3	2012-02-01	BISHAN	5 ROOM	289	BISHAN ST 24	07 TO 09	121.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	94.491	756685.821930
4	2011-12-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	86.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2011	93.877	637003.738935

5 rows × 33 columns

# also for the full dataset
hdb_price_df = pd.concat([hdb_price_df_03_12, hdb_price_df_13_23], axis=0)
print(hdb_price_df_03_12['month'].nunique() + hdb_price_df_13_23['month'].nunique() == hdb_price_df['month'].nunique())

True

hdb_price_df.head()

	month	town	flat_type	block	street_name	storey_range	floor_area_sqm	flat_model	lease_commence_date	remaining_lease	...	school_shortest_dist_name	good_sch_count_within_1km	good_sch_list_within_1km	good_sch_score	good_sch_score_sap_gep	distance_to_cbd_km	matured	year	CPI	adjusted_price
0	2012-09-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	97.298	657773.027195
1	2012-03-01	BISHAN	4 ROOM	289	BISHAN ST 24	01 TO 05	102.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	630205.762181
2	2012-03-01	BISHAN	5 ROOM	289	BISHAN ST 24	11 TO 15	121.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	766750.343987
3	2012-02-01	BISHAN	5 ROOM	289	BISHAN ST 24	07 TO 09	121.0	Premium Apartment	1998	85.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2012	94.491	756685.821930
4	2011-12-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	86.0	...	Catholic High School	1	['Catholic High School']	3	2	7.635584	1	2011	93.877	637003.738935

5 rows × 33 columns

# sanity check of adjusted prices
random_index = random.randint(0, len(hdb_price_df)+1)
print((hdb_price_df.iloc[random_index]['adjusted_price'] in hdb_price_df_03_12['adjusted_price'].to_list()) or 
      (hdb_price_df.iloc[random_index]['adjusted_price'] in hdb_price_df_13_23['adjusted_price'].to_list()))

True

hdb_price_df_03_12.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 278875 entries, 0 to 278874
Data columns (total 33 columns):
 #   Column                     Non-Null Count   Dtype         
---  ------                     --------------   -----         
 0   month                      278875 non-null  datetime64[ns]
 1   town                       278875 non-null  object        
 2   flat_type                  278875 non-null  object        
 3   block                      278875 non-null  object        
 4   street_name                278875 non-null  object        
 5   storey_range               278875 non-null  object        
 6   floor_area_sqm             278875 non-null  float64       
 7   flat_model                 278875 non-null  object        
 8   lease_commence_date        278875 non-null  int64         
 9   remaining_lease            278875 non-null  float64       
 10  resale_price               278875 non-null  float64       
 11  address                    278875 non-null  object        
 12  latitude                   278875 non-null  float64       
 13  longitude                  278875 non-null  float64       
 14  shortest_dist_mrt_name     278875 non-null  object        
 15  shortest_dist_mrt          278875 non-null  float64       
 16  mrt_count_within_1km       278875 non-null  int64         
 17  mrt_list_within_1km        278875 non-null  object        
 18  shortest_dist_mall_name    278875 non-null  object        
 19  shortest_dist_mall         278875 non-null  float64       
 20  mall_count_within_1km      278875 non-null  int64         
 21  mall_list_within_1km       278875 non-null  object        
 22  school_shortest_dist       278875 non-null  float64       
 23  school_shortest_dist_name  278875 non-null  object        
 24  good_sch_count_within_1km  278875 non-null  int64         
 25  good_sch_list_within_1km   278875 non-null  object        
 26  good_sch_score             278875 non-null  int64         
 27  good_sch_score_sap_gep     278875 non-null  int64         
 28  distance_to_cbd_km         278875 non-null  float64       
 29  matured                    278875 non-null  int64         
 30  year                       278875 non-null  int64         
 31  CPI                        278875 non-null  float64       
 32  adjusted_price             278875 non-null  float64       
dtypes: datetime64[ns](1), float64(11), int64(8), object(13)
memory usage: 72.3+ MB

# column names
hdb_price_df_03_12.columns

Index(['month', 'town', 'flat_type', 'block', 'street_name', 'storey_range',
       'floor_area_sqm', 'flat_model', 'lease_commence_date',
       'remaining_lease', 'resale_price', 'address', 'latitude', 'longitude',
       'shortest_dist_mrt_name', 'shortest_dist_mrt', 'mrt_count_within_1km',
       'mrt_list_within_1km', 'shortest_dist_mall_name', 'shortest_dist_mall',
       'mall_count_within_1km', 'mall_list_within_1km', 'school_shortest_dist',
       'school_shortest_dist_name', 'good_sch_count_within_1km',
       'good_sch_list_within_1km', 'good_sch_score', 'good_sch_score_sap_gep',
       'distance_to_cbd_km', 'matured', 'year', 'CPI', 'adjusted_price'],
      dtype='object')

4. Exploring the Relationship between Physical Characteristics of a Flat and HDB Resale Prices

The table below summarizes the physical features of a flat that we will be conducting EDA with — | No. | Feature Name | Values | | :---| :----: | :---: | | 1 | flat_type | 1 Room, 2 Room, ... , Executive, Multi-Generation| | 2 | year | 2003, 2004, ... , 2012 | | 3 | town | Bukit Timah, Woodlands, Dhoby Ghaut, ... | | 4 | storey_range | 01 to 03, 03 to 05, ... , 40 to 42| | 5 | floor_area_sqm | | | 6 | blocknum | | | 7 | flat_model | Simplified, New Generation, ..., Terrace, Premium Maisonette | | 8 | lease_commence_date | 1966, 1967, ..., 2010, 2012| | 9 | remaining_lease | |

4.1 Flat Type

Distribution of Flat Types

print('Unique Flat Types from 2003 to 2012:\n',hdb_price_df_03_12['flat_type'].unique())

Unique Flat Types from 2003 to 2012:
 ['4 ROOM' '5 ROOM' '3 ROOM' 'EXECUTIVE' '2 ROOM' '1 ROOM'
 'MULTI-GENERATION']

flat_count_04_13 = hdb_price_df_03_12.groupby(['flat_type']).size().reset_index()
flat_count_04_13.rename(columns={0: 'count'}, inplace=True)
flat_count_04_13['flat_type_proportion'] = (flat_count_04_13['count']/flat_count_04_13['count'].sum())*100
flat_count_04_13['flat_type_proportion'] = flat_count_04_13['flat_type_proportion'].round(1)
flat_count_04_13

	flat_type	count	flat_type_proportion
0	1 ROOM	205	0.1
1	2 ROOM	2818	1.0
2	3 ROOM	83878	30.1
3	4 ROOM	106622	38.2
4	5 ROOM	64450	23.1
5	EXECUTIVE	20781	7.5
6	MULTI-GENERATION	121	0.0

fig, ax = plt.subplots(figsize=(13.33, 7.5), dpi=96)

# Grid
# ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
#ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# Set colors for bar graph
flat_type_proportion_highest = flat_count_04_13['flat_type_proportion'].max()
colors=['#193441']*len(flat_count_04_13)
for index, row in flat_count_04_13.iterrows():
    if row['flat_type_proportion'] == flat_type_proportion_highest:
        colors[index] = '#448c79'
    else:
        colors[index] = '#193441'

# Plot bars
bar1 = ax.bar(flat_count_04_13['flat_type'], flat_count_04_13['flat_type_proportion'], width=0.6, color=colors)

# Label x-axis and x ticks
ax.set_xlabel('', fontsize=12, labelpad=10) # No need for an axis label
ax.xaxis.set_label_position("bottom")

# Reformat y-axis
ax.set_ylabel('Percentage of All Flat Types (%)', fontsize=12, labelpad=10)
ax.yaxis.set_label_position("left")
ax.yaxis.set_major_formatter(lambda s, i : f'{s:,.0f}')
ax.yaxis.set_major_locator(MaxNLocator(integer=True))
ax.yaxis.set_tick_params(pad=2, labeltop=False, labelbottom=True, bottom=False, labelsize=12)

# Add label on top of each bar
ax.bar_label(bar1, labels=[f'{e:,.0f}%' for e in flat_count_04_13['flat_type_proportion']], padding=3, color='black', fontsize=12) 

# Remove  spines
ax.spines[['top','left','bottom']].set_visible(False)

# Make right spine thicker
ax.spines['right'].set_linewidth(1.1)

# Add rectangle box and line (beautification purposes only)
ax.plot([0.08, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.08,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.08, y=.93, s="2003 to 2012: Distribution of HDB Flat Types", transform=fig.transFigure, ha='left', fontsize=16, weight='bold', alpha=.8)
ax.text(x=0.08, y=.90, s="Bar Graph — Proportions of the different flat types available in Singapore HDB Market", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None)

# Set a white background
fig.patch.set_facecolor('white')

Observations:

For the time period 2003 to 2012, 4 Room Flats take up the majority of flats in Singapore HDB market, accounting to almost 40% of all flat types. Followed by 3 Room, 5 Room and then Executive Flat Types.

There are proportionately very few 1 room, 2 rooms and multi-generation flat types. As such, we will remove the following flat types for subsequent visualizations:

• 1 Room
• 2 Room
• Multi-Generation

Comparing across both 10-year periods:

4 Room flats have increased in its proportion from 38% to 41% across, from time period 2003 to 2012 to time period 2013 to 2023. In the recent decade (i.e. 2013 to 2023), there is a more even proportion between 3 and 5 Room flats compared to the previous decade (i.e. 2003 to 2012). Executive Flat Types remains at a constant proportion of 8% of all Flat Types. 1 Room, 2 Room and Multi-Generation Flat Types remains almost negligble in terms of proportion across both decades. </font>

print('Unique flat types before removing:', hdb_price_df_03_12['flat_type'].unique(),'\n')
keep_flat_type = ['3 ROOM', '4 ROOM', '5 ROOM', 'EXECUTIVE']
hdb_price_df_03_12 = hdb_price_df_03_12[hdb_price_df_03_12['flat_type'].isin(keep_flat_type)]
print('Unique flat types after removing 1 rooms, 2 rooms and multi-generation flat types:',hdb_price_df_03_12['flat_type'].unique())

# do the same for 2013 to 2023
hdb_price_df_13_23 = hdb_price_df_13_23[hdb_price_df_13_23['flat_type'].isin(keep_flat_type)]

hdb_price_df = hdb_price_df[hdb_price_df['flat_type'].isin(keep_flat_type)]

Unique flat types before removing: ['4 ROOM' '5 ROOM' '3 ROOM' 'EXECUTIVE' '2 ROOM' '1 ROOM'
 'MULTI-GENERATION'] 

Unique flat types after removing 1 rooms, 2 rooms and multi-generation flat types: ['4 ROOM' '5 ROOM' '3 ROOM' 'EXECUTIVE']

Distribution of Resale Prices of Flat Types by Year

# create new feature flat_type_year
def get_year(x):
    return str(x)[:4]

hdb_price_df_03_12['flat_type_year'] = hdb_price_df_03_12['flat_type'] + ' - ' + hdb_price_df_03_12['month'].apply(lambda x: str(x)[:4])
hdb_price_df_03_12.head()

	month	town	flat_type	block	street_name	storey_range	floor_area_sqm	flat_model	lease_commence_date	remaining_lease	...	good_sch_count_within_1km	good_sch_list_within_1km	good_sch_score	good_sch_score_sap_gep	distance_to_cbd_km	matured	year	CPI	adjusted_price	flat_type_year
0	2012-09-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	85.0	...	1	['Catholic High School']	3	2	7.635584	1	2012	97.298	657773.027195	4 ROOM - 2012
1	2012-03-01	BISHAN	4 ROOM	289	BISHAN ST 24	01 TO 05	102.0	Premium Apartment	1998	85.0	...	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	630205.762181	4 ROOM - 2012
2	2012-03-01	BISHAN	5 ROOM	289	BISHAN ST 24	11 TO 15	121.0	Premium Apartment	1998	85.0	...	1	['Catholic High School']	3	2	7.635584	1	2012	95.207	766750.343987	5 ROOM - 2012
3	2012-02-01	BISHAN	5 ROOM	289	BISHAN ST 24	07 TO 09	121.0	Premium Apartment	1998	85.0	...	1	['Catholic High School']	3	2	7.635584	1	2012	94.491	756685.821930	5 ROOM - 2012
4	2011-12-01	BISHAN	4 ROOM	289	BISHAN ST 24	07 TO 09	100.0	Premium Apartment	1998	86.0	...	1	['Catholic High School']	3	2	7.635584	1	2011	93.877	637003.738935	4 ROOM - 2011

5 rows × 34 columns

# visualize using joypy library
import joypy

# time block: 2004 to 2013
fig, ax = joypy.joyplot(hdb_price_df_03_12,
                            by='flat_type_year',
                            column='adjusted_price',
                            figsize=(12,8),
                            title='Distribution of Resale Prices based on Flat Type for 2003 to 2012',
                            linewidth=0.05,
                            overlap=1.5,
                            alpha=0.8,
                            colormap=plt.cm.get_cmap('tab20',4))

ax[-1].set_xlim([0, 1400000])
ax[-1].set_xticklabels(['0', '200k', '400k', '600k', '800k', '1000k', '1200k']) 
plt.xlabel('Resale Price SGD ($)', fontsize=14)

plt.show()

Observations from the above Ridgeline Plot:

1. Consistent pattern throughout the years that HDB flats with more rooms tend to fetch higher resale prices, which is unsurprising.

2. Looking at how prices vary for the same number of rooms (i.e. same flat type), HDB resale prices generally increase over the years for the same flat type from 2003 to 2012.

4.2 Town

2003 to 2012

hdb_price_df_03_12['year'] = pd.DatetimeIndex(hdb_price_df_03_12['month']).year # extract out year
town_prices_03_12 = hdb_price_df_03_12.groupby(['town'], as_index=False).agg({'adjusted_price': 'median'}).sort_values('adjusted_price', ascending=True).reset_index(drop=True)
town_prices_03_12['adjusted_price'] = round(town_prices_03_12['adjusted_price']/1000) # convert prices in terms of SGD1000 for easier visualization since prices are all in '000s
town_prices_03_12

	town	adjusted_price
0	YISHUN	288.0
1	GEYLANG	302.0
2	ANG MO KIO	307.0
3	BEDOK	314.0
4	BUKIT BATOK	320.0
5	QUEENSTOWN	320.0
6	CLEMENTI	325.0
7	TOA PAYOH	335.0
8	JURONG EAST	339.0
9	JURONG WEST	342.0
10	KALLANG/WHAMPOA	345.0
11	WOODLANDS	347.0
12	HOUGANG	350.0
13	BUKIT PANJANG	356.0
14	SERANGOON	360.0
15	CENTRAL AREA	370.0
16	CHOA CHU KANG	370.0
17	TAMPINES	378.0
18	SEMBAWANG	385.0
19	BUKIT MERAH	401.0
20	MARINE PARADE	402.0
21	SENGKANG	422.0
22	PASIR RIS	431.0
23	BISHAN	460.0
24	PUNGGOL	468.0
25	BUKIT TIMAH	486.0

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

lowest_median_price, highest_median_price = town_prices_03_12['adjusted_price'].min(), town_prices_03_12['adjusted_price'].max()
colors=['#e3dfda']*len(town_prices_03_12)
# set to lowest and highest prices to red and green respectively
for index, row in town_prices_03_12.iterrows():
    if row['adjusted_price'] == lowest_median_price:
        colors[index] = '#e69798'
    if row['adjusted_price'] == highest_median_price:
        colors[index] = '#448c79'

# Grid
ax.grid(which="major", axis='x', alpha = 0.5,color='#DAD8D7', zorder=1)
ax.grid(which="major", axis='y', alpha=0.5, color='#DAD8D7', zorder=1)

# Plot bars
bar1=ax.barh(town_prices_03_12['town'], town_prices_03_12['adjusted_price'], color=colors)

# Label x-axis and x ticks
ax.set_xlabel("Median Resale Prices (SGD'000)", fontsize=12, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Add label on top of each bar
ax.bar_label(bar1, labels=[f'{e:,.0f}' for e in town_prices_03_12['adjusted_price']], padding=3, color='black', fontsize=10) 

# Remove spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

# Add rectangle box and line (beautification purposes only)
ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Median HDB Resale Prices by Town", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Horizontal Bar Graph — Comparing the prices of HDB across different Towns", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

Observations:

1. Bukit Timah is the most expensive town across 2003 to 2012. It has the highest median HDB Resale Price of SGD$486,000.

2. Yishun has the lowest median resale price of SGD$288,000, indicating that it is the least expensive town across 2003 to 2012.

Comparing across both 10-year periods:

Bukit Timah remains the most expensive town across both decades, from 2003 all the way to 2023 and it has increased in its median price. </font>

2003 vs 2023: Percentage Change of HDB Resale Prices by Town

town_prices_04_23 = hdb_price_df[hdb_price_df['year'].isin([2003,2023])].groupby(['town','year'], as_index=False).agg({'adjusted_price': 'median'})
town_prices_04_23['change'] = town_prices_04_23.groupby('town')['adjusted_price'].apply(lambda x: x.pct_change()*100).round() # calculate percentage change between 2004 and 2023
town_prices_04_23 = town_prices_04_23[town_prices_04_23['change'].notnull()] 
town_prices_04_23 = town_prices_04_23.sort_values('change', ascending=True).reset_index(drop=True)

town_prices_04_23

	town	year	adjusted_price	change
0	JURONG EAST	2023	411540.705764	29.0
1	PASIR RIS	2023	570033.304308	40.0
2	MARINE PARADE	2023	471287.377577	45.0
3	CHOA CHU KANG	2023	475775.828793	47.0
4	TAMPINES	2023	502706.536083	48.0
5	BEDOK	2023	386006.804492	49.0
6	SENGKANG	2023	511683.438513	51.0
7	BUKIT TIMAH	2023	585742.883561	53.0
8	HOUGANG	2023	480264.280008	54.0
9	SERANGOON	2023	473980.448307	55.0
10	SEMBAWANG	2023	483855.040980	58.0
11	BISHAN	2023	662495.399338	61.0
12	WOODLANDS	2023	475775.828793	61.0
13	GEYLANG	2023	382864.888642	64.0
14	BUKIT PANJANG	2023	475775.828793	65.0
15	ANG MO KIO	2023	403960.609352	69.0
16	JURONG WEST	2023	457822.023932	69.0
17	TOA PAYOH	2023	457822.023932	80.0
18	CENTRAL AREA	2023	505798.181280	81.0
19	YISHUN	2023	421914.414212	84.0
20	CLEMENTI	2023	500462.310475	99.0
21	KALLANG/WHAMPOA	2023	534125.694588	111.0
22	BUKIT BATOK	2023	538614.145803	114.0
23	BUKIT MERAH	2023	658904.638365	160.0
24	QUEENSTOWN	2023	610429.365243	174.0

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

smallest_price_change, largest_price_change = town_prices_04_23['change'].min(), town_prices_04_23['change'].max()
colors=['#e3dfda']*len(town_prices_04_23)
# set to lowest and highest prices to red and green respectively
for index, row in town_prices_04_23.iterrows():
    if row['change'] == smallest_price_change:
        colors[index] = '#e69798'
    if row['change'] == largest_price_change:
        colors[index] = '#448c79'

# Plot bars
bar1 = ax.barh(town_prices_04_23['town'], town_prices_04_23['change'], color=colors)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# Label x-axis and x ticks
ax.set_xlabel("Percentage Change in Median Resale Prices (%)", fontsize=13, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Add label on top of each bar
ax.bar_label(bar1, labels=[f'{e:,.0f}' for e in town_prices_04_23['change']], padding=3, color='black', fontsize=11) 

# Remove the spines and make the right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)


# Add in red line and rectangle on top
ax.plot([0, 0.9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0,0.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0, y=.93, s="2003 vs 2023: Percentage Change of HDB Resale Prices by Town", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0, y=.91, s="Horizontal Bar Graph — How HDB resale prices changed in the last two decades, based on the town's median price", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)


plt.show()

Observations:

Comparing the percentage change in median HDB resale prices between 2003 and this year, 2023, all the towns in Singapore has generally increased in its median HDB resale prices.

Queenstown has the highest percentage change in its resale prices (157% which is more than double) while Jurong East has the lowest increase in resale prices, at only 29%.

Past Two Years (2022 and 2023): Percentage Change of Resale Prices by Town

town_prices_22_23 = hdb_price_df[hdb_price_df['year'].isin([2022,2023])].groupby(['town','year'], as_index=False).agg({'adjusted_price': 'median'})
town_prices_22_23

	town	year	adjusted_price
0	ANG MO KIO	2022	395947.680293
1	ANG MO KIO	2023	403960.609352
2	BEDOK	2022	410962.607810
3	BEDOK	2023	386006.804492
4	BISHAN	2022	643169.539491
5	BISHAN	2023	662495.399338
6	BUKIT BATOK	2022	450218.677643
7	BUKIT BATOK	2023	538614.145803
8	BUKIT MERAH	2022	624537.762297
9	BUKIT MERAH	2023	658904.638365
10	BUKIT PANJANG	2022	467684.780458
11	BUKIT PANJANG	2023	475775.828793
12	BUKIT TIMAH	2022	659954.263717
13	BUKIT TIMAH	2023	585742.883561
14	CENTRAL AREA	2022	555236.976962
15	CENTRAL AREA	2023	505798.181280
16	CHOA CHU KANG	2022	478633.431848
17	CHOA CHU KANG	2023	475775.828793
18	CLEMENTI	2022	489400.846482
19	CLEMENTI	2023	500462.310475
20	GEYLANG	2022	419613.518655
21	GEYLANG	2023	382864.888642
22	HOUGANG	2022	471274.889205
23	HOUGANG	2023	480264.280008
24	JURONG EAST	2022	418703.294103
25	JURONG EAST	2023	411540.705764
26	JURONG WEST	2022	464151.158107
27	JURONG WEST	2023	457822.023932
28	KALLANG/WHAMPOA	2022	624148.542663
29	KALLANG/WHAMPOA	2023	534125.694588
30	MARINE PARADE	2022	449443.230573
31	MARINE PARADE	2023	471287.377577
32	PASIR RIS	2022	560912.222812
33	PASIR RIS	2023	570033.304308
34	PUNGGOL	2022	511008.800389
35	PUNGGOL	2023	520660.340943
36	QUEENSTOWN	2022	644864.420804
37	QUEENSTOWN	2023	610429.365243
38	SEMBAWANG	2022	484866.567838
39	SEMBAWANG	2023	483855.040980
40	SENGKANG	2022	501448.107151
41	SENGKANG	2023	511683.438513
42	SERANGOON	2022	495746.454346
43	SERANGOON	2023	473980.448307
44	TAMPINES	2022	502917.480831
45	TAMPINES	2023	502706.536083
46	TOA PAYOH	2022	489653.518975
47	TOA PAYOH	2023	457822.023932
48	WOODLANDS	2022	466314.696363
49	WOODLANDS	2023	475775.828793
50	YISHUN	2022	427030.053426
51	YISHUN	2023	421914.414212

town_prices_22_23 = hdb_price_df[hdb_price_df['year'].isin([2021,2022])].groupby(['town','year'], as_index=False).agg({'adjusted_price': 'median'})
town_prices_22_23['change'] = town_prices_22_23.groupby('town')['adjusted_price'].apply(lambda x: x.pct_change()*100).round() # calculate percentage change between 2004 and 2023
town_prices_22_23 = town_prices_22_23[town_prices_22_23['change'].notnull()] 
town_prices_22_23 = town_prices_22_23.sort_values('change', ascending=True).reset_index(drop=True).reset_index()
town_prices_22_23

	index	town	year	adjusted_price	change
0	0	CENTRAL AREA	2022	555236.976962	-16.0
1	1	GEYLANG	2022	419613.518655	-13.0
2	2	QUEENSTOWN	2022	644864.420804	-7.0
3	3	CLEMENTI	2022	489400.846482	-7.0
4	4	MARINE PARADE	2022	449443.230573	-6.0
5	5	JURONG EAST	2022	418703.294103	-5.0
6	6	BEDOK	2022	410962.607810	-4.0
7	7	BUKIT TIMAH	2022	659954.263717	-4.0
8	8	SERANGOON	2022	495746.454346	-1.0
9	9	BUKIT MERAH	2022	624537.762297	-1.0
10	10	BUKIT PANJANG	2022	467684.780458	-1.0
11	11	TAMPINES	2022	502917.480831	1.0
12	12	YISHUN	2022	427030.053426	1.0
13	13	HOUGANG	2022	471274.889205	1.0
14	14	CHOA CHU KANG	2022	478633.431848	2.0
15	15	TOA PAYOH	2022	489653.518975	2.0
16	16	PASIR RIS	2022	560912.222812	3.0
17	17	BISHAN	2022	643169.539491	3.0
18	18	JURONG WEST	2022	464151.158107	4.0
19	19	PUNGGOL	2022	511008.800389	4.0
20	20	SENGKANG	2022	501448.107151	5.0
21	21	WOODLANDS	2022	466314.696363	6.0
22	22	ANG MO KIO	2022	395947.680293	7.0
23	23	SEMBAWANG	2022	484866.567838	8.0
24	24	BUKIT BATOK	2022	450218.677643	8.0
25	25	KALLANG/WHAMPOA	2022	624148.542663	19.0

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# smallest_price_change, largest_price_change = town_prices_22_23['change'].min(), town_prices_22_23['change'].max()
colors=['#e3dfda']*len(town_prices_22_23)
# set to lowest and highest prices to red and green respectively
for index, row in town_prices_22_23.iterrows():
    if row['change'] < 0:
        colors[index] = '#e69798'
    if row['change'] > 0:
        colors[index] = '#448c79'

# Plot bars
bar1 = ax.barh(town_prices_22_23['town'], town_prices_22_23['change'], color=colors)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# Label x-axis and x ticks
ax.set_xlabel("Percentage Change in Median Resale Prices (%)", fontsize=13, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Add label on top of each bar
ax.bar_label(bar1, labels=[f'{e:,.0f}' for e in town_prices_22_23['change']], padding=3, color='black', fontsize=11) 

# Remove the spines
ax.spines[['top','left','bottom']].set_visible(False)

# Make the right spine thicker
ax.spines['right'].set_linewidth(1.1)

# Add in red line and rectangle on top
ax.plot([0, 0.9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0,0.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0, y=.93, s="2022 vs 2023: Percentage Change of HDB Resale Prices by Town", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0, y=.91, s="Horizontal Bar Graph — How HDB resale prices changed in the past two years, based on the town's median price", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

Observations:

The changes are not very significant from 2022 to 2023, generally increase/decrease by about 10%.

Prices have dropped the most (16%) in Central Area while on the other hand, prices in Kallang/Whampoa have increased the most, by 19%.

Could it be because these areas have older HDB meaning their lease are now shorter? As shown below, it seems that places like Punggol, and Sengkang, tend to have later lease commence date, as they were developed later, which might have led to their slight increase in prices, while places like Toa Payoh and Central Area, tend to have older lease commence date.

hdb_price_df[hdb_price_df['year'].isin([2022,2023])].groupby('town')['lease_commence_date'].median().sort_values()

town
MARINE PARADE      1975.0
ANG MO KIO         1980.0
BEDOK              1980.0
CLEMENTI           1981.0
GEYLANG            1982.0
TOA PAYOH          1984.0
CENTRAL AREA       1984.0
JURONG EAST        1984.0
SERANGOON          1986.0
BUKIT BATOK        1987.0
YISHUN             1988.0
BISHAN             1988.0
TAMPINES           1988.0
BUKIT TIMAH        1988.0
HOUGANG            1991.0
PASIR RIS          1993.0
BUKIT MERAH        1996.0
KALLANG/WHAMPOA    1996.0
JURONG WEST        1997.0
WOODLANDS          1998.0
BUKIT PANJANG      1999.0
CHOA CHU KANG      1999.0
QUEENSTOWN         2003.0
SEMBAWANG          2004.0
SENGKANG           2012.0
PUNGGOL            2014.0
Name: lease_commence_date, dtype: float64

4.3 Storey Range

# Storey Prices for 2003 to 2012
storey_03_12 = hdb_price_df_03_12.groupby('storey_range')['adjusted_price'].median().reset_index().sort_values(by='storey_range')
storey_03_12['storey_rank'] = storey_03_12['storey_range'].astype('category').cat.codes + 1 # label encode storey -> give each storey range a rank
storey_03_12

	storey_range	adjusted_price	storey_rank
0	01 TO 03	329708.612238	1
1	01 TO 05	437878.582524	2
2	04 TO 06	338049.172898	3
3	06 TO 10	456597.127412	4
4	07 TO 09	345384.585079	5
5	10 TO 12	352215.687015	6
6	11 TO 15	480799.024156	7
7	13 TO 15	404257.405596	8
8	16 TO 18	448980.485775	9
9	16 TO 20	562986.748960	10
10	19 TO 21	491767.117778	11
11	21 TO 25	659750.517144	12
12	22 TO 24	530184.790249	13
13	25 TO 27	578703.492919	14
14	26 TO 30	750120.144591	15
15	28 TO 30	629168.948303	16
16	31 TO 33	747245.324440	17
17	31 TO 35	738105.685451	18
18	34 TO 36	767685.064524	19
19	36 TO 40	764202.383311	20
20	37 TO 39	775556.873218	21
21	40 TO 42	731579.864135	22

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Set colors
colors = ['#193441']*len(storey_03_12)
for index, row in storey_03_12.iterrows():
    if row['adjusted_price'] > 700000:
        colors[index] = '#e69798'

scatter1=sns.scatterplot(x=storey_03_12['storey_range'], y=storey_03_12['adjusted_price'], s=storey_03_12['storey_rank'].astype('int')*30, sizes=(20,200),color=colors, edgecolors='w', alpha=0.5, ax=ax)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# Label data points
for i, row in storey_03_12.iterrows():
    # offset = row['storey_rank'] / 50
    label = str(round(row['adjusted_price']/1000)) + 'K'
    plt.annotate(label, xy=(row['storey_range'], row['adjusted_price']), ha='center', va='bottom', xytext=(0, 14), textcoords='offset points', fontsize=11)

# Label x-axis and x ticks
ax.set_xlabel("Percentage Change in Median Resale Prices (%)", fontsize=12, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)
plt.xticks(rotation=45, fontsize=10)

# Reformat y-axis
ylabels = ['{:,.0f}'.format(x) + 'K' for x in scatter1.get_yticks()/1000]
scatter1.set_yticklabels(ylabels)
scatter1.set_ylim([300000,850000])
scatter1.set_ylabel('Resale Price SGD ($)', size=15)
scatter1.set_xlabel('Storey', size=15)

# Remove the spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Storey and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Bubble Scatterplot — How HDB resale prices are associated with floor level, based on the median price of each storey range", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/3416411860.py:28: UserWarning: FixedFormatter should only be used together with FixedLocator
  scatter1.set_yticklabels(ylabels)

Observations:

For HDB Flats between 1st and 25th floor, as floor level increases, the median HDB resale prices are also associated with an increase. Above 25th floor, the prices remain generally the same as seen from the plateau (red) in the scatter plot.

However, it is noticeable that the labels for the X axis are not well represented as there are overlaps in the storey level. For example, '01 TO '03' is contained within the label '01 TO 05'. Hence, we will adjust the labels of the X axis accordingly.

For example: | Old Storey Range | New Storey Range | | :---: | :----: |
| 01 TO 03 | 01 TO 05 | | 01 TO 05 | 01 TO 05 |

hdb_price_df_03_12['storey_range'].sort_values().unique()

array(['01 TO 03', '01 TO 05', '04 TO 06', '06 TO 10', '07 TO 09',
       '10 TO 12', '11 TO 15', '13 TO 15', '16 TO 18', '16 TO 20',
       '19 TO 21', '21 TO 25', '22 TO 24', '25 TO 27', '26 TO 30',
       '28 TO 30', '31 TO 33', '31 TO 35', '34 TO 36', '36 TO 40',
       '37 TO 39', '40 TO 42'], dtype=object)

hdb_price_df_03_12['storey_range'].nunique()

22

Adjustment of Labels in the X Axis

storey_03_12_dict = {'storey_range':[],
                     'adjusted_price':[]}

for index, row in hdb_price_df_03_12.iterrows():
    # print(row['storey_range'], row['adjusted_price'])
    storey, price = row['storey_range'][:2], row['adjusted_price']

    if int(storey) in range(1,6): 
        storey_range = '01 TO 05'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(6,11): 
        storey_range = '06 TO 10'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(11,16): 
        storey_range = '11 TO 15'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(16,21): 
        storey_range = '16 TO 20'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(21,26): 
        storey_range = '21 TO 25'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(26,31): 
        storey_range = '26 TO 30'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(31,36): 
        storey_range = '31 TO 35'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) in range(36,41): 
        storey_range = '36 TO 40'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

    if int(storey) > 40:
        storey_range = '> 40'
        storey_03_12_dict['storey_range'].append(storey_range)
        storey_03_12_dict['adjusted_price'].append(price)

storey_03_12 = pd.DataFrame.from_dict(storey_03_12_dict)
storey_03_12

	storey_range	adjusted_price
0	06 TO 10	657773.027195
1	01 TO 05	630205.762181
2	11 TO 15	766750.343987
3	06 TO 10	756685.821930
4	06 TO 10	637003.738935
...	...	...
275726	06 TO 10	219344.889929
275727	06 TO 10	227070.524652
275728	01 TO 05	186425.556281
275729	06 TO 10	217717.817505
275730	01 TO 05	174121.087260

275731 rows × 2 columns

storey_03_12 = storey_03_12.groupby('storey_range')['adjusted_price'].median().reset_index().sort_values(by='storey_range')
storey_03_12['storey_rank'] = storey_03_12['storey_range'].astype('category').cat.codes + 1 # label encode storey -> give each storey range a rank
storey_03_12

	storey_range	adjusted_price	storey_rank
0	01 TO 05	336531.401439	1
1	06 TO 10	350926.989124	2
2	11 TO 15	409633.745418	3
3	16 TO 20	463389.727871	4
4	21 TO 25	552616.108172	5
5	26 TO 30	638049.068471	6
6	31 TO 35	758554.351685	7
7	36 TO 40	766985.283967	8

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Set colors
colors = ['#193441']*len(storey_03_12)
for index, row in storey_03_12.iterrows():
    if row['adjusted_price'] > 700000 or row['adjusted_price'] < 400000:
        colors[index] = '#e69798'

scatter1=sns.scatterplot(x=storey_03_12['storey_range'], y=storey_03_12['adjusted_price'], s=storey_03_12['storey_rank'].astype('int')*30, sizes=(20,200),color=colors, edgecolors='w', alpha=0.5, ax=ax)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# Label data points
for i, row in storey_03_12.iterrows():
    # offset = row['storey_rank'] / 50
    label = str(round(row['adjusted_price']/1000)) + 'K'
    plt.annotate(label, xy=(row['storey_range'], row['adjusted_price']), ha='center', va='bottom', xytext=(0, 14), textcoords='offset points', fontsize=11)

# Label x-axis and x ticks
ax.set_xlabel("Percentage Change in Median Resale Prices (%)", fontsize=12, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)
plt.xticks(rotation=45, fontsize=10)

# Reformat y-axis
ylabels = ['{:,.0f}'.format(x) + 'K' for x in scatter1.get_yticks()/1000]
scatter1.set_yticklabels(ylabels)
scatter1.set_ylim([300000,850000])
scatter1.set_ylabel('Resale Price SGD ($)', size=15)
scatter1.set_xlabel('Storey', size=15)

# Remove the spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Storey and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Bubble Scatterplot — How HDB resale prices are associated with floor level, based on the median price of each storey range", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/1759168102.py:28: UserWarning: FixedFormatter should only be used together with FixedLocator
  scatter1.set_yticklabels(ylabels)

Observations:

After adjusting for overlappping Storey Ranges (i.e. x-axis labels), there is a clearer trend observed. As floor level increases, HDB Resale Prices are generally associated with an increase as well. However, the rate of increase at the lower and higher floors (indicated in red) are almost negligible compared to that in the middle floors.

Comparing across both 10-year periods:

For 2003 to 2012, at the high floors, the HDB Resale Prices tend to plateau and does not increase as much compared to the middle floors. However, for 2013 to 2023, HDB Resale Prices continuously increase as floor level increases, regardless of how high the floor gets. In fact, above 37th floor, the rate of increase of prices is still increasing, and HDB Flats in those floors are fetching close to or more than SGD$1 Million. </font>

4.4 Floor Area

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.regplot(x='floor_area_sqm', y='adjusted_price', data=hdb_price_df_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylabel('Resale Price SGD ($)', size=15)
p.set_xlabel('Floor Area (Square Meters)', size=15)

# Remove the spines and make right thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Floor Area and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot & Regression Line — How HDB resale prices are associated with floor area (in sqm)", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/63038850.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

As floor area of a HDB resale flat increases, its resale price is associated with an increase as well. Majority of transacted HDB resale flats have a floor area of between 50 and 200 square metres.

Comparing across both 10-year periods:

Not much visible difference between the steepness of graphs showing the relationship between Floor Area and HDB Resale Prices across both decades.

</font>

Using Median Prices for each Floor Area

floor_area_03_12 = hdb_price_df_03_12.groupby(['floor_area_sqm'], as_index=False).agg({'adjusted_price':'median'})
floor_area_03_12 # 167 data points

	floor_area_sqm	adjusted_price
0	49.0	207312.016780
1	51.0	252503.126358
2	52.0	216487.918979
3	53.0	219766.013832
4	54.0	254647.014645
...	...	...
163	249.0	875602.884018
164	250.0	875404.874755
165	261.0	803462.620740
166	266.0	766829.167725
167	280.0	730702.383798

168 rows × 2 columns

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.regplot(x='floor_area_sqm', y='adjusted_price', data=floor_area_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylabel('Resale Price SGD ($)', size=15)
p.set_xlabel('Floor Area (Square Meters)', size=15)

# Remove the spines and make right thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Floor Area and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot & Regression Line — using Median HDB Resale Price for each Floor Area", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/3282878973.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

# what are those HDBs which have bigger than 200 sqm of floor area
above_200_sqm_df = hdb_price_df_03_12[hdb_price_df_03_12['floor_area_sqm'] > 200].reset_index()
above_200_sqm_df.groupby('flat_model')['index'].nunique()

flat_model
Apartment              2
Maisonette             5
Premium Maisonette     2
Terrace               22
Name: index, dtype: int64

Observations:

Trend is similar to without taking median prices for each floor area; previous visualization.

Flat Models of HDBs which have larger than 200 sqm of floor area are mostly Terrace, which is not surprising.

Comparing across both 10-year periods:

Not much visible difference between the steepness of graphs showing the relationship between Floor Area and HDB Resale Prices across both decades.

</font>

4.5 Block Number

3 digit system was introduced in the 1970s, with the 1st digit representing a neighbourhood in a town. So for e.g., AMK neighbourhood 1 starts with 101, and AMK neighbourhood 2 starts with 201. So first digit was separated from last 2 digits and plotted separately

import re

# Block Number Prices
get_num = lambda x: int(re.findall("\d+", x)[0])
hdb_price_df_03_12['blocknum'] = hdb_price_df_03_12['block'].apply(get_num) # get only digits from block number
tmp = hdb_price_df_03_12[hdb_price_df_03_12['blocknum'] > 99] # get only blocks that use 3-digit numbering system
tmp = tmp.groupby('blocknum')['adjusted_price'].median().reset_index()

fig, ax = plt.subplots(figsize=(20, 13), dpi=96)

ax1 = plt.subplot(121)
a=sns.scatterplot(x=tmp['blocknum'].apply(lambda x: int(str(x)[0])), y=tmp['adjusted_price'], color='#193441', edgecolors='w', alpha=0.9)
ylabels = ['{:,.0f}'.format(x) + 'K' for x in a.get_yticks()/1000]
ax1.set_yticklabels(ylabels)
ax1.set_ylabel('Resale Price ($SGD)', size=15), ax1.set_xlabel('Neighbourhood Number', size=15)

ax2 = plt.subplot(122)
b=sns.scatterplot(x=tmp['blocknum'].apply(lambda x: int(str(x)[1:])), y=tmp['adjusted_price'], color = '#e69798',edgecolors='w', alpha=0.9)
ax2.set_yticklabels(ylabels)
ax2.set_ylabel('', size=15)
ax2.set_xlabel('Block Number', size=15)

# Add in line and rectangle on top
plt.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax1.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
plt.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Neighbourhood/Block Number and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
plt.text(x=0.05, y=.91, s="Scatterplot — How (if any) HDB Resale Price is associated with Neighbourhood/Block Number", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/3980874740.py:14: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax1.set_yticklabels(ylabels)
/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/3980874740.py:19: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_yticklabels(ylabels)

Observations:

Neighbourhood/Block Number does not seem to have any association with HDB Resale Prices.

Comparing across both 10-year periods:

Not much visible difference in the scatter plots across both decades.

</font>

4.6 Flat Model

fig, ax = plt.subplots(figsize=(12, 14), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.violinplot(x='adjusted_price', 
                 y='flat_model', 
                 data=hdb_price_df_03_12, 
                 width=1,
                 orient='h', # horizontal plot
                 order=hdb_price_df_03_12.groupby('flat_model')['adjusted_price'].median().sort_values().reset_index()['flat_model'].tolist(),
                 palette='Set3')

# Label x-axis and x ticks
p.set_xlabel("Resale Price (SGD$)", fontsize=14, labelpad=10) 
xlabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_xticks()/1000]
p.set_xticklabels(xlabels)
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)
p.set_xlim([0,1300000])

# Reformat y-axis
# ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
# p.set_yticklabels(ylabels)
p.set_ylabel("Flat Model", size=15)
# p.set_ylim([50000,1300000])

# Remove the spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.94, s="2003 to 2012: Relationship between Flat Models and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.92, s="Violin Boxplot — Distribution of HDB Resale Prices across different Flat Models", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/1127161346.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_xticklabels(xlabels)

Observations:

Flat Models like Terrace and Premium Maisonette have relatively high median resale prices, compared to Simplified, New Generation and Standard flat models. Adjoined Flat has the largest range. Oddly, some flat models have negative resale prices.

Comparing across both 10-year periods:

Not much visible difference between the violin plot distributions showing the relationship between Flat Model and HDB Resale Prices across both decades.

</font>

x = np.array((hdb_price_df_03_12['adjusted_price'] < 0).to_list())
np.unique(x)

array([False])

4.7 Lease Commence Date (in Years)

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

a=sns.boxplot(y='lease_commence_date', x='adjusted_price', data=hdb_price_df_03_12, width=1, orient='h', flierprops = dict(markerfacecolor = 'red', markersize = 0.1, linestyle='none'), linewidth=0.4)
ax.set_xlabel('Resale Price ($SGD)', size=15)
xlabels = ['{:,.0f}'.format(x) + 'K' for x in a.get_xticks()/1000]
ax.set_xticklabels(xlabels)
p.set_xlim([0,1300000])

ax.set_ylabel('Lease Commence Year', size=15)


# Remove the spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Lease Commence (Year) and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Boxplot — Distribution of Resale Prices amoung HDB Flats with different years of Lease Commencement", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/2707222080.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax.set_xticklabels(xlabels)

Observations:

HDB Flats with earlier Lease Commence Dates generally fetch lower Median Resale Prices compared to those that have more recent Lease Commence Dates.

Comparing across both 10-year periods:

Not much visible difference between the boxplot distributions showing the relationship between Lease Commence and HDB Resale Prices across both decades.

</font>

4.8 Remaining Lease (in Years)

hdb_price_df['remaining_lease'].unique()

array([ 85.        ,  86.        ,  87.        ,  88.        ,
        89.        ,  90.        ,  91.        ,  92.        ,
        93.        ,  94.        ,  70.        ,  71.        ,
        72.        ,  73.        ,  74.        ,  75.        ,
        76.        ,  77.        ,  78.        ,  79.        ,
        80.        ,  81.        ,  82.        ,  83.        ,
        84.        ,  65.        ,  66.        ,  67.        ,
        68.        ,  69.        ,  95.        ,  96.        ,
        54.        ,  55.        ,  56.        ,  57.        ,
        58.        ,  59.        ,  60.        ,  61.        ,
        62.        ,  63.        ,  64.        ,  97.        ,
        98.        ,  99.        , 100.        , 101.        ,
        74.33333333,  74.58333333,  74.66666667,  75.08333333,
        75.58333333,  76.5       ,  76.66666667,  76.75      ,
        79.08333333,  80.16666667,  59.41666667,  60.16666667,
        60.33333333,  60.91666667,  60.83333333,  61.08333333,
        61.25      ,  61.41666667,  61.66666667,  62.33333333,
        62.5       ,  62.75      ,  63.33333333,  63.5       ,
        63.58333333,  64.33333333,  65.16666667,  65.33333333,
        65.5       ,  62.91666667,  63.25      ,  63.66666667,
        64.16666667,  66.83333333,  67.33333333,  67.58333333,
        94.75      ,  94.91666667,  64.08333333,  64.66666667,
        64.75      ,  64.91666667,  65.41666667,  65.66666667,
        65.58333333,  66.33333333,  66.58333333,  68.33333333,
        61.91666667,  63.41666667,  63.91666667,  64.41666667,
        65.25      ,  65.83333333,  66.5       ,  66.91666667,
        60.66666667,  66.66666667,  64.25      ,  64.83333333,
        67.5       ,  67.75      ,  67.83333333,  67.91666667,
        68.16666667,  68.41666667,  68.83333333,  69.41666667,
        63.75      ,  63.83333333,  64.58333333,  67.08333333,
        67.25      ,  67.16666667,  68.5       ,  68.58333333,
        68.66666667,  59.16666667,  59.91666667,  60.08333333,
        63.08333333,  64.5       ,  54.25      ,  55.5       ,
        55.58333333,  56.08333333,  56.25      ,  56.33333333,
        56.66666667,  58.33333333,  59.66666667,  59.75      ,
        62.41666667,  62.58333333,  63.16666667,  57.66666667,
        57.75      ,  58.16666667,  58.25      ,  58.75      ,
        58.66666667,  59.58333333,  60.41666667,  61.33333333,
        61.83333333,  62.16666667,  62.66666667,  66.16666667,
        66.75      ,  67.41666667,  68.75      ,  54.91666667,
        55.25      ,  55.66666667,  56.16666667,  57.16666667,
        58.91666667,  60.58333333,  62.25      ,  65.08333333,
        66.41666667,  69.08333333,  75.66666667,  76.16666667,
        76.41666667,  76.83333333,  77.08333333,  77.16666667,
        77.33333333,  77.41666667,  78.25      ,  78.5       ,
        78.58333333,  78.75      ,  79.41666667,  79.33333333,
        79.75      ,  80.25      ,  81.08333333,  81.25      ,
        81.33333333,  81.41666667,  81.5       ,  80.08333333,
        80.41666667,  81.16666667,  82.16666667,  82.25      ,
        82.33333333,  82.66666667,  83.75      ,  83.83333333,
        84.33333333,  84.5       ,  84.75      ,  84.83333333,
        84.91666667,  85.08333333,  85.58333333,  85.75      ,
        85.83333333,  75.75      ,  76.08333333,  76.25      ,
        77.5       ,  77.66666667,  77.83333333,  77.91666667,
        78.33333333,  79.25      ,  80.75      ,  81.58333333,
        69.33333333,  69.75      ,  69.91666667,  70.16666667,
        70.33333333,  71.91666667,  71.83333333,  72.83333333,
        73.75      ,  74.16666667,  74.41666667,  69.25      ,
        70.91666667,  73.66666667,  75.91666667,  75.83333333,
        77.25      ,  77.75      ,  79.58333333,  79.91666667,
        81.83333333,  61.16666667,  66.25      ,  69.83333333,
        92.08333333,  92.58333333,  92.66666667,  92.91666667,
        93.16666667,  93.58333333,  93.83333333,  93.91666667,
        94.16666667,  94.33333333,  94.25      ,  94.41666667,
        94.5       ,  94.58333333,  94.66666667,  95.16666667,
        95.25      ,  92.33333333,  92.41666667,  92.75      ,
        93.08333333,  94.08333333,  76.91666667,  78.08333333,
        78.16666667,  78.83333333,  78.91666667,  79.16666667,
        79.66666667,  68.08333333,  65.91666667,  69.58333333,
        70.08333333,  70.83333333,  71.16666667,  71.33333333,
        62.83333333,  87.83333333,  87.91666667,  88.08333333,
        88.25      ,  89.08333333,  89.33333333,  89.5       ,
        89.75      ,  90.33333333,  90.41666667,  91.33333333,
        92.16666667,  93.25      ,  93.41666667,  93.66666667,
        55.41666667,  55.91666667,  56.41666667,  56.5       ,
        56.58333333,  57.91666667,  59.33333333,  59.25      ,
        55.08333333,  55.75      ,  55.83333333,  58.58333333,
        73.91666667,  74.5       ,  74.83333333,  75.33333333,
        75.41666667,  81.91666667,  82.08333333,  82.41666667,
        82.5       ,  82.83333333,  83.66666667,  84.41666667,
        84.66666667,  73.16666667,  73.41666667,  73.5       ,
        73.58333333,  73.83333333,  75.5       ,  76.58333333,
        77.58333333,  78.66666667,  69.66666667,  70.58333333,
        70.66666667,  71.08333333,  71.25      ,  71.58333333,
        74.25      ,  74.75      ,  93.75      ,  94.83333333,
        95.08333333,  88.41666667,  88.58333333,  88.5       ,
        89.16666667,  89.83333333,  90.16666667,  90.5       ,
        91.16666667,  91.58333333,  92.25      ,  93.5       ,
        73.25      ,  74.91666667,  75.25      ,  83.16666667,
        83.25      ,  83.33333333,  83.41666667,  83.58333333,
        84.16666667,  80.5       ,  80.66666667,  80.91666667,
        81.75      ,  82.91666667,  84.08333333,  85.25      ,
        71.5       ,  71.66666667,  72.5       ,  72.41666667,
        72.58333333,  56.83333333,  59.08333333,  59.83333333,
        60.25      ,  61.5       ,  61.75      ,  62.08333333,
        57.08333333,  57.33333333,  61.58333333,  56.75      ,
        57.25      ,  57.41666667,  57.5       ,  57.58333333,
        57.83333333,  60.5       ,  54.5       ,  54.83333333,
        55.16666667,  58.08333333,  58.5       ,  54.58333333,
        59.5       ,  60.75      ,  44.16666667,  44.25      ,
        44.41666667,  44.66666667,  44.83333333,  44.91666667,
        46.58333333,  46.75      ,  47.        ,  48.08333333,
        48.5       ,  48.58333333,  48.91666667,  49.        ,
        50.        ,  52.        ,  53.        ,  45.5       ,
        47.08333333,  47.91666667,  48.75      ,  51.        ,
        67.66666667,  45.08333333,  45.16666667,  45.58333333,
        45.91666667,  46.08333333,  46.16666667,  46.5       ,
        46.41666667,  47.33333333,  47.83333333,  47.75      ,
        48.        ,  49.5       ,  49.91666667,  50.5       ,
        50.66666667,  50.83333333,  45.33333333,  45.66666667,
        46.33333333,  47.16666667,  48.16666667,  49.08333333,
        49.58333333,  49.75      ,  49.83333333,  46.66666667,
        46.91666667,  47.5       ,  49.41666667,  49.33333333,
        49.66666667,  53.25      ,  53.33333333,  54.08333333,
        54.41666667,  56.91666667,  58.41666667,  65.75      ,
        93.33333333,  95.41666667,  71.41666667,  72.75      ,
        66.08333333,  47.66666667,  48.33333333,  50.08333333,
        50.33333333,  50.41666667,  50.75      ,  51.33333333,
        51.58333333,  51.75      ,  51.83333333,  52.08333333,
        52.25      ,  52.66666667,  52.91666667,  72.91666667,
        78.41666667,  79.83333333,  82.58333333,  83.08333333,
        83.91666667,  79.5       ,  80.33333333,  80.58333333,
        80.83333333,  81.66666667,  84.25      ,  50.16666667,
        50.58333333,  51.91666667,  52.16666667,  52.41666667,
        52.5       ,  52.58333333,  53.5       ,  53.91666667,
        54.33333333,  54.75      ,  58.83333333,  88.83333333,
        88.91666667,  89.58333333,  90.58333333,  90.83333333,
        91.5       ,  92.5       ,  86.91666667,  87.5       ,
        87.58333333,  87.66666667,  87.75      ,  88.16666667,
        88.33333333,  88.75      ,  89.41666667,  89.91666667,
        90.08333333,  90.25      ,  90.66666667,  90.75      ,
        90.91666667,  91.66666667,  91.75      ,  91.83333333,
        92.83333333,  82.75      ,  84.58333333,  85.5       ,
        86.25      ,  86.75      ,  87.08333333,  87.16666667,
        87.25      ,  87.41666667,  85.33333333,  85.41666667,
        85.66666667,  50.91666667,  52.33333333,  52.75      ,
        53.08333333,  53.16666667,  72.33333333,  71.75      ,
        72.25      ,  70.5       ,  72.66666667,  73.33333333,
        95.33333333,  95.5       ,  95.91666667,  74.08333333,
        48.66666667,  48.83333333,  49.25      ,  50.25      ,
        51.08333333,  51.41666667,  51.5       ,  52.83333333,
        53.66666667,  53.83333333,  48.41666667,  51.16666667,
        53.41666667,  53.75      ,  91.91666667,  95.75      ,
        95.66666667,  42.66666667,  44.08333333,  46.25      ,
        91.25      ,  91.41666667,  88.66666667,  91.08333333,
        70.41666667,  72.08333333,  68.25      ,  70.25      ,
        53.58333333,  69.5       ,  55.33333333,  51.25      ,
        51.66666667,  54.16666667,  68.91666667,  69.16666667,
        54.66666667,  83.5       ,  85.16666667,  86.16666667,
        86.33333333,  86.66666667,  85.91666667,  86.41666667,
        86.5       ,  86.58333333,  89.25      ,  87.33333333,
        89.66666667,  95.58333333,  73.08333333,  75.16666667,
        46.83333333,  86.83333333,  86.08333333,  76.33333333,
        70.75      ,  47.58333333,  48.25      ,  45.41666667,
        49.16666667,  43.33333333,  43.66666667,  43.75      ,
        44.75      ,  46.        ,  45.        ,  45.75      ,
        95.83333333,  96.58333333,  72.16666667,  96.33333333,
        96.75      ,  47.25      ,  47.41666667,  45.25      ,
        45.83333333,  43.41666667,  43.58333333,  43.83333333,
        44.58333333,  96.16666667,  96.5       ,  44.33333333,
        96.08333333,  43.08333333,  43.25      ,  43.5       ,
        43.        ,  44.5       ,  44.        ,  96.66666667,
        97.16666667,  96.25      ,  96.41666667,  43.16666667,
        43.91666667,  97.25      ,  97.41666667,  96.91666667,
        97.33333333,  97.08333333,  97.75      ,  96.83333333,
        97.58333333])

hdb_price_df['remaining_lease'].nunique() 

661

remaining_lease_04_13 = hdb_price_df_03_12[['remaining_lease','adjusted_price']]
print(remaining_lease_04_13['adjusted_price'].min(),remaining_lease_04_13['adjusted_price'].max())

99687.64537781618 1077064.5754671313

remaining_lease_04_13

	remaining_lease	adjusted_price
0	85.0	657773.027195
1	85.0	630205.762181
2	85.0	766750.343987
3	85.0	756685.821930
4	86.0	637003.738935
...	...	...
278867	74.0	219344.889929
278868	74.0	227070.524652
278869	74.0	186425.556281
278870	74.0	217717.817505
278874	76.0	174121.087260

275731 rows × 2 columns

remaining_lease_04_13.nunique()

remaining_lease       48
adjusted_price     56894
dtype: int64

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# 2004 to 2013
p=sns.regplot(x='remaining_lease', y='adjusted_price', data=remaining_lease_04_13, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})

# Label x-axis and x ticks
ax.set_xlabel("Remaining Lease (in Years)", fontsize=14, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Reformat y-axis
p.set_ylabel('Resale Price (SGD$)', size=15)
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylim([50000,1100000])

# Remove the spines and make right thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

# Add in line and rectangle on top
ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Remaining Lease Left and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot and Regression Line — How (if any) HDB Resale Price is associated with Remaining Number of Years of Lease", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/2214177128.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

Newer HDB Flats (i.e. longer remaining lease) are associated with higher Resale Prices.

Comparing across both 10-year periods:

Not much visible difference between the steepness of graphs showing the relationship between Remaining Lease and HDB Resale Prices across both decades.

</font>

5. Exploring the Relationship between Locational Features and HDB Resale Prices

The table below summarizes the locational features of a flat that we will be conducting EDA with — | No. | Feature Name | Values | | :---| :----: | :---: | | 1 | mrt_count_within_1km | 1, 2, 3, ...| | 2 | mall_count_within_1km | 1, 2, 3, ...| | 3 | good_sch_count_within_1km | 1, 2, 3, ...| | 4 | shortest_dist_mrt | 1, 2, 3, ...| | 5 | shortest_dist_mall | 1, 2, 3, ...| | 6 | good_sch_score | 1, 2, 3, ...| | 7 | distance_to_cbd_km | 1, 2, 3, ...| | 8 | matured | 0 or 1|

5.1 Number of Amenities within 1 km

Amenities are defined as MRT Stations, Shopping Malls and Good Schools.

import re

fig, ax = plt.subplots(figsize=(16,8), dpi=96)

# Number of MRT stations within 1 km
ax1 = plt.subplot(131)
a=sns.regplot(x='mrt_count_within_1km', y='adjusted_price', data=hdb_price_df_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})
ylabels = ['{:,.0f}'.format(x) + 'K' for x in a.get_yticks()/1000]
ax1.set_yticklabels(ylabels)
ax1.set_ylabel('Resale Price (SGD$)', size=15)
ax1.set_xlabel('Number of MRT Stations', size=15)

# Number of Malls within 1 km
ax2 = plt.subplot(132)
b=sns.regplot(x='mall_count_within_1km', y='adjusted_price', data=hdb_price_df_03_12, color = '#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})
ax2.set_yticklabels(ylabels)
ax2.set_ylabel('', size=15)
ax2.set_xlabel('Number of Malls', size=15)

# Number of Good Schools within 1 km
ax3 = plt.subplot(133)
c=sns.regplot(x='good_sch_count_within_1km', y='adjusted_price', data=hdb_price_df_03_12, color = '#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})
ax3.set_yticklabels(ylabels)
ax3.set_ylabel('', size=15)
ax3.set_xlabel('Number of Good Schools', size=15)

# Add in line and rectangle on top
ax1.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax1.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax1.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Number of Amenities within 1km and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax1.text(x=0.05, y=.90, s="Scatterplot & Regression Line — How (if any) HDB Resale Price is associated with Number of Nearby Amenities like MRT Stations, Shopping Malls and Good Schools", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/881328384.py:9: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax1.set_yticklabels(ylabels)
/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/881328384.py:16: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax2.set_yticklabels(ylabels)
/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/881328384.py:23: UserWarning: FixedFormatter should only be used together with FixedLocator
  ax3.set_yticklabels(ylabels)

Observations:

(Relative Comparison)

Number of MRT Stations within 1km vs HDB Resale Price:

• Weak and postiive correlation

Number of Malls within 1km vs HDB Resale Price:

• Strong and postiive correlation

Number of Good Schools within 1km vs HDB Resale Price:

• Very weak (little to no) and positive correlation

Comparing across both 10-year periods:

We can see a trend that having more MRT Stations that are nearby HDB Flats, are starting to get significantly more popular in the recent decade as shown in the increase in steepness of the regression line (2013 to 2023 graph). We cannot really say the same for the number of nearby malls and good schools.

</font>

5.2 Distance to Nearest MRT (in km)

print(hdb_price_df_03_12['shortest_dist_mrt'].min(), hdb_price_df_03_12['shortest_dist_mrt'].max())

7.395791802037197

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.regplot(x='shortest_dist_mrt', y='adjusted_price', data=hdb_price_df_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})

# Label x-axis and x ticks
ax.set_xlabel("Shortest Distance to nearest MRT (km)", fontsize=14, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Reformat y-axis
p.set_ylabel('Resale Price SGD ($)', size=15)
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylim([50000,1200000])

# Remove the spines and Make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Distance to Nearest MRT and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot & Regression Line — how HDB resale prices vary with distance to nearest MRT", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/3421450413.py:16: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

General trend is that the further away the HDB is from the nearest MRT, it is associated with a decrease in its resale price.

Surprisingly, some HDBs are more than 7km away from the nearest MRT have a relatively high resale price. Let's see which town(s) these HDBs belong to

Comparing across both 10-year periods:

It is safe to say that all HDBs are within about 4km to the nearest MRT Station in the recent decade, unlike in 2003 to 2012, where there are numerous HDB flats that are as far as more than 7km away from the nearest MRT Station. </font>

towns_more_than_7km_list = hdb_price_df_03_12[hdb_price_df_03_12['shortest_dist_mrt'] > 7]['town'].unique()
towns_more_than_7km = ''.join([str(town) for town in towns_more_than_7km_list])
print('Town(s) with HDBs that are more than 7km away from the nearest MRT Station:\n',towns_more_than_7km)

Town(s) with HDBs that are more than 7km away from the nearest MRT Station:
 MARINE PARADE

before 2013, there was no nearby MRT in the Marina Parade area

5.3 Distance to Nearest Shopping Mall

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.regplot(x='shortest_dist_mall', y='adjusted_price', data=hdb_price_df_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})


# Label x-axis and x ticks
ax.set_xlabel("Distance to Nearest Shopping Mall (km)", fontsize=14, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Reformat y-axis
p.set_ylabel('Resale Price (SGD$)', size=15)
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylim([50000,1200000])

# Remove the spines and Make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Distance to Nearest Shopping Mall and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot & Regression Line — how HDB resale prices vary with distance to nearest Shopping Mall", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/570600936.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

There is a relatively strong and negative association between Distance to Nearest Shopping Mall and HDB Resale Prices. In other words, HDB Flats that are further away from their Nearest Shopping Mall are generally associated with a lower Resale Price.

Comparing across both 10-year periods:

Not much visible difference between the steepness of graphs showing the relationship between Distance to Nearest Shopping Mall and HDB Resale Prices across both decades.

However, there seems to be numerous HDB Flats that are more than 6km away from the Nearest Shopping Mall that have relatively high resale prices. Let's see which town(s) these HDBs belong to and what are the exact HDBs features. </font>

hdb_more_than_6km_from_nearest_mall = hdb_price_df_03_12[hdb_price_df_03_12['shortest_dist_mall'] > 6]
hdb_more_than_6km_from_nearest_mall

	month	town	flat_type	block	street_name	storey_range	floor_area_sqm	flat_model	lease_commence_date	remaining_lease	...	good_sch_list_within_1km	good_sch_score	good_sch_score_sap_gep	distance_to_cbd_km	matured	year	CPI	adjusted_price	flat_type_year	blocknum
80115	2011-11-01	MARINE PARADE	5 ROOM	27	MARINE CRES	19 TO 21	126.0	Standard	1975	63.0	...	[]	0	0	23.277334	1	2011	93.842	815200.017050	5 ROOM - 2011	27
80116	2011-09-01	MARINE PARADE	5 ROOM	27	MARINE CRES	22 TO 24	126.0	Standard	1975	63.0	...	[]	0	0	23.277334	1	2011	92.941	874748.496358	5 ROOM - 2011	27
80117	2011-08-01	MARINE PARADE	5 ROOM	27	MARINE CRES	04 TO 06	126.0	Standard	1975	63.0	...	[]	0	0	23.277334	1	2011	93.115	757128.282232	5 ROOM - 2011	27
80118	2010-10-01	MARINE PARADE	5 ROOM	27	MARINE CRES	07 TO 09	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	88.525	787348.206721	5 ROOM - 2010	27
80119	2010-08-01	MARINE PARADE	5 ROOM	27	MARINE CRES	13 TO 15	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	88.081	749310.293934	5 ROOM - 2010	27
80120	2010-07-01	MARINE PARADE	5 ROOM	27	MARINE CRES	01 TO 03	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	87.676	612482.321274	5 ROOM - 2010	27
80121	2010-07-01	MARINE PARADE	5 ROOM	27	MARINE CRES	22 TO 24	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	87.676	858843.925362	5 ROOM - 2010	27
80122	2010-07-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	87.676	804096.902231	5 ROOM - 2010	27
80123	2010-04-01	MARINE PARADE	5 ROOM	27	MARINE CRES	01 TO 03	126.0	Standard	1975	64.0	...	[]	0	0	23.277334	1	2010	86.901	605286.475415	5 ROOM - 2010	27
80124	2009-04-01	MARINE PARADE	5 ROOM	27	MARINE CRES	25 TO 27	126.0	Standard	1975	65.0	...	[]	0	0	23.277334	1	2009	84.206	783792.128827	5 ROOM - 2009	27
80125	2008-09-01	MARINE PARADE	5 ROOM	27	MARINE CRES	22 TO 24	126.0	Standard	1975	66.0	...	[]	0	0	23.277334	1	2008	85.361	743899.438854	5 ROOM - 2008	27
80126	2008-06-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	126.0	Standard	1975	66.0	...	[]	0	0	23.277334	1	2008	84.296	753297.902629	5 ROOM - 2008	27
80127	2008-06-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	126.0	Standard	1975	66.0	...	[]	0	0	23.277334	1	2008	84.296	744993.831261	5 ROOM - 2008	27
80128	2008-05-01	MARINE PARADE	5 ROOM	27	MARINE CRES	07 TO 09	126.0	Standard	1975	66.0	...	[]	0	0	23.277334	1	2008	84.451	674947.602752	5 ROOM - 2008	27
80129	2008-05-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	123.0	Standard	1975	66.0	...	[]	0	0	23.277334	1	2008	84.451	686788.788765	5 ROOM - 2008	27
80130	2007-12-01	MARINE PARADE	5 ROOM	27	MARINE CRES	01 TO 03	126.0	Standard	1975	67.0	...	[]	0	0	23.277334	1	2007	81.147	603842.409454	5 ROOM - 2007	27
80131	2007-06-01	MARINE PARADE	5 ROOM	27	MARINE CRES	07 TO 09	126.0	Standard	1975	67.0	...	[]	0	0	23.277334	1	2007	78.384	586854.460094	5 ROOM - 2007	27
80132	2007-02-01	MARINE PARADE	5 ROOM	27	MARINE CRES	19 TO 21	126.0	Standard	1975	67.0	...	[]	0	0	23.277334	1	2007	77.479	639011.861279	5 ROOM - 2007	27
80133	2006-09-01	MARINE PARADE	5 ROOM	27	MARINE CRES	22 TO 24	126.0	Standard	1975	68.0	...	[]	0	0	23.277334	1	2006	77.627	611900.498538	5 ROOM - 2006	27
80134	2006-07-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	126.0	Standard	1975	68.0	...	[]	0	0	23.277334	1	2006	77.651	553759.771284	5 ROOM - 2006	27
80135	2006-03-01	MARINE PARADE	5 ROOM	27	MARINE CRES	07 TO 09	126.0	Standard	1975	68.0	...	[]	0	0	23.277334	1	2006	77.252	515197.017553	5 ROOM - 2006	27
80136	2005-12-01	MARINE PARADE	5 ROOM	27	MARINE CRES	04 TO 06	126.0	Standard	1975	69.0	...	[]	0	0	23.277334	1	2005	77.534	506874.403488	5 ROOM - 2005	27
80137	2005-04-01	MARINE PARADE	5 ROOM	27	MARINE CRES	13 TO 15	126.0	Standard	1975	69.0	...	[]	0	0	23.277334	1	2005	76.843	553075.751858	5 ROOM - 2005	27
80138	2004-09-01	MARINE PARADE	5 ROOM	27	MARINE CRES	10 TO 12	126.0	Standard	1975	70.0	...	[]	0	0	23.277334	1	2004	76.816	544157.467194	5 ROOM - 2004	27
80139	2004-07-01	MARINE PARADE	5 ROOM	27	MARINE CRES	22 TO 24	126.0	Standard	1975	70.0	...	[]	0	0	23.277334	1	2004	76.758	612314.025900	5 ROOM - 2004	27
80140	2004-01-01	MARINE PARADE	5 ROOM	27	MARINE CRES	01 TO 03	126.0	Standard	1975	70.0	...	[]	0	0	23.277334	1	2004	75.741	547919.884871	5 ROOM - 2004	27
80141	2004-01-01	MARINE PARADE	5 ROOM	27	MARINE CRES	19 TO 21	128.0	Standard	1975	70.0	...	[]	0	0	23.277334	1	2004	75.741	607332.884435	5 ROOM - 2004	27
80142	2003-09-01	MARINE PARADE	5 ROOM	27	MARINE CRES	25 TO 27	126.0	Standard	1975	71.0	...	[]	0	0	23.277334	1	2003	75.392	663200.339559	5 ROOM - 2003	27
80143	2003-05-01	MARINE PARADE	5 ROOM	27	MARINE CRES	19 TO 21	126.0	Standard	1975	71.0	...	[]	0	0	23.277334	1	2003	74.981	653498.886385	5 ROOM - 2003	27

29 rows × 35 columns

towns_more_than_6km_list = hdb_more_than_6km_from_nearest_mall['town'].unique()
towns_more_than_6km = ''.join([str(town) for town in towns_more_than_6km_list])

street_name_more_than_6km_list = hdb_more_than_6km_from_nearest_mall['street_name'].unique()
street_name_more_than_6km = ''.join([str(street_name) for street_name in street_name_more_than_6km_list])

print('Town(s) and corresponding Street Name(s) of HDBs that are more than 6km away from the nearest Shopping Mall:\n Town:',towns_more_than_6km,'\nStreet Name:',street_name_more_than_6km )

Town(s) and corresponding Street Name(s) of HDBs that are more than 6km away from the nearest Shopping Mall:
 Town: MARINE PARADE 
Street Name: MARINE CRES

5.4 Good Schools Score

hdb_price_df['good_sch_score'].unique()

array([3, 4, 1, 0, 2])

fig, ax = plt.subplots(figsize=(12, 14), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p=sns.violinplot(x='good_sch_score',
                 y='adjusted_price', 
                 data=hdb_price_df_03_12, 
                 width=1, 
                 palette='Set3',
                 order=hdb_price_df_03_12.groupby('good_sch_score')['adjusted_price'].median().reset_index()['good_sch_score'].tolist())


p.set_xticklabels(p.get_xticklabels(), rotation=0, ha='right')
p.set_xlabel('Good Schools Score', size=15)
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylabel('Resale Price (SGD$)', size=15)

# Remove the spines and make right spine thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

# Add in  line and rectangle on top
ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Good School Score and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=18, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Violin Boxplot — How HDB resale prices vary with different Good Schools Score", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/588363344.py:20: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

The scoring of good schools within 1 km of HDB Flat does not seem to be correlated with its resale price, as seen by the similar distribution of resale prices in the violin plot and corresponding median resale prices.

Comparing across both 10-year periods:

Not much visible difference between the distributions of HDB resale prices for the different Good Schools Score. </font>

5.5 Distance to Central Business District (CBD)

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

# 2004 to 2013
p=sns.regplot(x='distance_to_cbd_km', y='adjusted_price', data=hdb_price_df_03_12, color='#e69798', scatter_kws={"s": 1, 'alpha':0.5, 'color':'#193441'})

# Label x-axis and x ticks
ax.set_xlabel("Distance to CBD (in km)", fontsize=14, labelpad=10) 
ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0)

# Reformat y-axis
p.set_ylabel('Resale Price SGD ($)', size=15)
ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylim([50000,1300000])

# Remove the spines
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

# Add in  line and rectangle on top
ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Distance to CBD and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=18, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Scatterplot & Regression Line — How HDB resale prices vary with distance to Central Business District (CBD)", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

# Adjust the margins around the plot area
plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.9, wspace=None, hspace=None)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/4227616796.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

Weak and negative correlation between Distance to CBD and HDB Resale Prices. The further the distance of HDB Flats from the Central Business District, there is a slight association that resale prices are lower and vice-versa.

Comparing across both 10-year periods:

In the recent decade (i.e. from 2013 to 2023), there is a stronger negative correlation between Distance to CBD and HDB Resale Prices compared to the earlier decade (i.e. 2003 to 2012); the steeper gradient in the recent decade implies that HDB Flats that are further away from CBD are associated with a more than proportionate dip in its prices. This suggests the likelihood of changing preferences in HDB owners' when it comes to the proximity of the HDB Flats and Central Business District. </font>

5.6 Maturity of HDB Estate

hdb_price_df['matured'].unique()

array([1, 0])

fig, ax = plt.subplots(figsize=(13.33, 13), dpi=96)

# Grid
ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)

p = sns.boxplot(x='matured', y='adjusted_price', data = hdb_price_df_03_12, palette=['#e69798', '#e69798'])
p.set_xticklabels(['Not Matured', 'Matured'], size = 15)
p.set_xlabel('', size=15)

ylabels = ['{:,.0f}'.format(x) + 'K' for x in p.get_yticks()/1000]
p.set_yticklabels(ylabels)
p.set_ylabel('HDB Resale Price (SGD$)')
p.set_ylim([0,1200000])

# Remove the spines and make right thicker
ax.spines[['top','left','bottom']].set_visible(False)
ax.spines['right'].set_linewidth(1.1)

ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#193441', linewidth=.6)
ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#193441', transform=fig.transFigure, clip_on=False, linewidth = 0))

# Add in title and subtitle
ax.text(x=0.05, y=.93, s="2003 to 2012: Relationship between Maturity of Estate and HDB Resale Prices", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8)
ax.text(x=0.05, y=.91, s="Boxplot — Investigate whether or not HDB Flats in Mature Estates are associated with its Resale Prices", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8)

plt.show()

/var/folders/dr/gs9wbbgs2zlcfyqybn5vvrw40000gn/T/ipykernel_8839/2501376037.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator
  p.set_yticklabels(ylabels)

Observations:

Not much visible association between maturity of estates and resale prices. Median HDB Resale Prices are similar for both matured and non-matured estates.

Comparing across both 10-year periods:

In the recent decade (i.e. from 2013 to 2023), Matured Estates tend to fetch a slightly higher Median HDB Resale Prices compared to Non-Matured Estates while Median Prices are more or less the same regardless of the Estate's Maturity for the previous decade (i.e. 2003 to 2012). </font>